How EFL Learners Fill in the Blanks of an X-test: Think-Aloud Protocol
Bibliographic record
Abstract
Since SLA literature remains researchers unaware of the mental processes involved in the X-Test taking (in contrast to C-Test which there are plenty of available related studies), this article aims at exploring cognitive strategies that EFL learners may use while answering an English X-test, which like the C-Test has been modified, adapted and used in many research papers. To this aim, thirty EFL respondents from Mashhad, Iran, were randomly asked to answer a reliable and valid X-test. All of them participated in introspective methods of think-aloud and retrospective interviews during and after the test administration. To analyze the data only the exact word scoring procedure was employed. The results showed participants used various cognitive strategies in taking the X-Test. It was also revealed that respondents experienced more strategies when filling out an X-Test comparing to related literature of C-test, which could be an indicator of the importance job of cognition in X-Test taking. It is hoped that the article can shed light on the underling cognitive strategies that English language learners’ use, and provide a chance for educators who want to better understand the learners’ cognitive processes in order to assist them identify problems and improve their English instruction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".